Comparison of Information-Gain Criteria for Action Selection
This work addresses action selection for robotic manipulators, but it is incremental as it compares existing criteria without introducing a new method.
The paper tackles the problem of selecting actions for active tactile data collection in object pose estimation by empirically evaluating various information gain criteria, finding similar pose accuracy with sparse measurements across all criteria.
Accurate object pose estimation using multi-modal perception such as visual and tactile sensing have been used for autonomous robotic manipulators in literature. Due to variation in density of visual and tactile data, a novel probabilistic Bayesian filter-based approach termed translation-invariant Quaternion filter (TIQF) is proposed for pose estimation using point cloud registration. Active tactile data collection is preferred by reasoning over multiple potential actions for maximal expected information gain as tactile data collection is time consuming. In this paper, we empirically evaluate various information gain criteria for action selection in the context of object pose estimation. We demonstrate the adaptability and effectiveness of our proposed TIQF pose estimation approach with various information gain criteria. We find similar performance in terms of pose accuracy with sparse measurements (<15 points) across all the selected criteria. Furthermore, we explore the use of uncommon information theoretic criteria in the robotics domain for action selection.